Simultaneous shooting of seismic sources makes it possible to sample a subsurface region more effectively and efficiently. During simultaneous source shooting, multiple sources can be activated inside a single conventional shotpoint time window. Benefits of firing multiple shots within a short time period include shortening overall acquisition time and increasing spatial sampling bandwidth. However, energy from any individual shot can interfere with energy from time-adjacent shots, which allows sources to interfere with each other and generate blending noise. Thus, major technical challenges of simultaneous source shooting include separating sources (“deblending”) and forming interference-free records. In general, deblending problem is underdetermined, requiring extra assumptions and/or regularization to obtain a unique solution.
In recent years, compressive sensing (CS) theory has seen some adoption within the oil and gas industry. Applications of CS theory can significantly broaden seismic data bandwidth and reduce seismic acquisition cost. While traditional seismic exploration methods rely on higher fold to improve data quality, compressive sensing provides a method for recovering coarsely sampled data. CS is an emerging field in signal processing, which requires much fewer measurements compared to Shannon's sampling criterion (Candes et al., 2006; Baraniuk, 2007). CS theory suggests that successful signal recovery can be best achieved through random measurements together with sparsity of true signal.